While radio frequency (RF) fingerprinting based upon Wi-Fi or cellular signals has been a popular approach to indoor localization in research literature, its adoption in the real world has been stymied by the need for site-specific calibration. Specifically, to perform localization based upon observed strengths of signals received from wireless transmitters in an indoor environment, a set of training data is necessary. Generally, the set of training data includes known static locations of wireless transmitters in the indoor environment, known locations of mobile computing devices in the indoor environment, and observed signal strength measurements for signals emitted by the wireless transmitters and observed at the wireless computing devices when the wireless computing devices are at respective known locations. An exemplary data packet that can be included in the set of training data for enabling RF fingerprinting in an indoor environment can include location coordinates of a mobile computing device in the indoor environment, a list of wireless transmitters in the indoor environment, and respective received signal strength (RSS) measurements generated at the mobile computing device at the location coordinates. Once a sufficient amount of training data has been acquired, a RF map (fingerprint) of the indoor environment can be generated.
A major deterrent in creating an RF map for an indoor environment is the requirement, in conventional approaches, of needing a relatively significant amount of user participation to collect the set of training data. Specifically, people must travel over an entirety of a floor space of an indoor environment, which may be relatively large (such as an airport or a shopping center), to collect RSS measurements from various locations in the indoor environment. In an exemplary approach, to obtain RSS measurements at known locations, a set of users is provided with a map of the indoor environment, which is presented on a display screen of a respective user's mobile computing device. As the user is moving in the indoor environment, such user manually indicates her position by selecting particular portions of the map. In this manner, RSS measurements can be mapped to locations of the indoor environment. It can be ascertained, however, that a significant amount of user effort is required to obtain such mapping of RSS measurements to locations, particularly on a relatively large scale.